Exploring the interplay between antiretroviral therapy and the gut-oral microbiome axis in people living with HIV

The gut and oral microbiome is altered in people living with HIV (PLWH). While antiretroviral treatment (ART) is pivotal in restoring immune function in PLWH, several studies have identified an association between specific antiretrovirals, particularly integrase inhibitors (INSTI), and weight gain. In our study, we explored the differences in the oral and gut microbiota of PLWH under different ART regimens, and its correlation to Body Mass Index (BMI). Fecal and salivary samples were collected from PLWH (n = 69) and healthy controls (HC, n = 80). We performed taxonomy analysis to determine the microbial composition and relationship between microbial abundance and ART regimens, BMI, CD4+T-cell count, CD4/CD8 ratio, and ART duration. PLWH showed significantly lower richness compared to HC in both the oral and gut environment. The gut microbiome composition of INSTI-treated individuals was enriched with Faecalibacterium and Bifidobacterium, whereas non-nucleotide reverse transcriptase inhibitor (NNRTI)-treated individuals were enriched with Gordonibacter, Megasphaera, and Staphylococcus. In the oral microenvironment, Veillonella was significantly more abundant in INSTI-treated individuals and Fusobacterium and Alloprevotella in the NNRTI-treated individuals. Furthermore, Bifidobacterium and Dorea were enriched in gut milieu of PLWH with high BMI. Collectively, our findings identify distinct microbial profiles, which are associated with different ART regimens and BMI in PLWH on successful ART, thereby highlighting significant effects of specific antiretrovirals on the microbiome.


Sequence analysis
Paired end Illumina reads were checked for quality using FastQC 24 and trimmed using Cutadapt 25 .The taxonomic classification and analysis of the trimmed reads were performed using dada2 26 within Qiime2 27 in combination with SILVAv132 database 28 .Alpha diversity of the samples was estimated using the R function estimate_richness in R package phyloseq (v1.30.0) 29 and visualized using R package ggplot2 (v3.3.5) 30 .The diversity indices such as Observed, Shannon, and Simpson were performed to calculate the richness and diversity of the samples.The samples were clustered based on the distance method Bray-Curtis and visualized using non-metric multidimensional scaling (NMDS) ordination plots.The significance of the different factors on the beta-diversity were calculated based on PERMANOVA using vegan package (v2.5.7) (Adonis function).Linear discriminant analysis Effect Size (LEfSe) was employed to determine the significant microbial communities between the groups with LDA score > 2 and P < 0.05 31 and visualized using R package ggplot2.Correlation analysis was performed using Spearman correlation method using R package psych (v2.2.3) 32 and results were visualized using R package ggplot2 (v3.3.5).

Ethics approval and consent to participate
The Swedish Ethical Review Authority (ID 2021-00,451, ID 2023-05,153-02) thoroughly examined and granted approval for the ethical permit, and every participant duly furnished written informed consent.
Since mode of transmission (MOT) has been identified as one of the factors influencing microbiome in PLWH 3,13 , we stratified the individuals with different MOT (MSM vs Heterosexuals) into separate treatment groups.The same microbiome markers were not associated with MOT groups but varied within treatment groups (data not shown).This implies that MOT was not the major driver of microbiome changes in our cohort.

Relationship between gut microbiota composition and BMI
Based on the potential clinical association between INSTI treatment and weight gain reported in few studies 19,20 , we further explored the link between microbiome, ART, and BMI in our cohort.In the gut microbiome of PLWH, Succinivibrio (p = 0.045), Dorea (p = 0.004), and Bifidobacterium (p = 0.03) were significantly higher in individuals with high BMI (> 25) and Escherichia-Shigella (p = 0.01), Bacteroides (p = 0.04) and Klebsiella (p = 0.03) were enriched in group with low BMI (< 25).In oral samples, we observed higher abundance of Prevotella (p = 0.02), Dialister (p = 0.004), and Veillonella (p = 0.01) in PLWH with overweight and Neisseria (p = 0.03) in PLWH with low BMI (Fig. 3A, B).Similar microbial signatures were also observed in individuals with high and low BMI belonging to the whole cohort (PLWH and HC) in both oral and gut samples (Fig S2).These signatures were most likely shaped by PLWH status, since stratifying the HC group into high and low BMI have not revealed similar associations.
DTG has been primarily associated with visceral fat accumulation 33 .As nearly 70% of all PLWH were treated with DTG, we sub-categorized DTG-treated individuals based on low and high BMI.In the fecal samples Bifidobacterium (p = 0.01), Dorea (p = 0.03), and Streptococcus (p = 0.01) were significantly more abundant in people with high BMI, while Bacteroides (p = 0.047) and Escherichia-Shigella (p = 0.045) were more abundant in people with low BMI.

Effect of DTG on the gut and oral microbiota
We conducted a more in-depth analysis of the associations between the microbiome and several clinical factors, such as age, duration of treatment, and CD4 + T-cell counts in PLWH on DTG.In the gut milieu, alpha diversity was lower in the younger individuals (18-39 years) compared to the elderly (> 60 years) (p < 0.01, Fig S3 A).We also found significant differences in beta-diversity among the age groups (p = 0.05) (Fig S3 B).At the genus level, younger individuals displayed a significantly greater abundance of Lachnospira (p = 0.04) and Eggerthella (p < 0.0001), while elderly individuals harbored a higher abundance of Coprococcus (p = 0.01) and Dorea (p = 0.01) (Fig S3 C).However, for oral samples, we found no significant differences in alpha and beta diversity between the age groups (Fig S3 D, E).We also found that Kingella was significantly abundant in younger individuals and Leptotrichia and Ruminococcaceae UCG-004 were abundant in the middle-aged group (40-59 years) (Fig S3 F).
Moreover, in the DTG-treated group, PLWH with longer treatment duration exhibited significantly higher alpha diversity indices in fecal microbiome compared to those on short-term ART (Fig S4 A), with a higher abundance of Succinivibrio (p = 0.034) (Fig S4 B).On the other hand, the alpha diversity hasn't changed significantly in between the individuals during longitudinal follow-up and short-term follow-up in the oral compartment (Fig S4 C), although the saliva samples of individuals under long-term ART had a higher prevalence of Dialister (p = 0.04) (Fig S4 D).

Discussion
In our work, we investigated the shifts within the gut and oral microbiome of PLWH in relation to different ART components and immune status.Furthermore, we explored the correlation between microbiome, antiretroviral treatments, and BMI, since weight gain is reported as a potential adverse outcome associated with certain ART regimens 19,20,[33][34][35] .
Initially, we observed that PLWH had significantly lower richness and different bacterial composition compared to HC, in both the oral and gut environments.These differences were present even if the PLWH had been on efficient long-term ART with sustained high CD4 + T-cell counts and undetectable HIV RNA.Specifically, we noted an enrichment of Bifidobacterium, Lachnospira, Akkermansia, and Faecalibacterium in the gut microbiome of HCs.On the contrary, there were increased levels of potentially pathogenic bacteria such as Succinivibrio, Megasphaera, Klebsiella, Escherichia-Shigella, and Ruminococcus gnavus group in PLWH.Moreover, bacterium such as Bifidobacterium are known for their probiotic qualities and play a critical role in the effective functioning of the immune system 36 .Similarly, Faecalibacterium and Akkermansia possess anti-inflammatory properties and are instrumental in governing immune activation, host metabolism, and the preservation of gut barrier integrity 37 .Additionally, Lachnospira is known to produce beneficial metabolites conducive to gut health 38 .Our findings suggest that Bifidobacterium, Lachnospira, Akkermansia, and Faecalibacterium may serve as markers of a healthy gut.Conversely, Escherichia-Shigella and Klebsiella, whilst common gut commensals, have the potential to become opportunistic pathogens in individuals with compromised immune system 39 .Escherichia-Shigella produces various proinflammatory components such as lipopolysaccharide and peptidoglycans which could contribute to excessive intestinal inflammation 40 .The presence of these bacteria suggests the increased abundance of certain pathobionts in the gut of PLWH.In the oral environment, we found that Bulleidia was enriched in PLWH, which is more frequently observed in individuals with periodontitis 41 .Conversely, bacteria such as Leptotrichia and Selenomonas were increased in HC.Studies have shown that both these taxa are a part of the normal oral microbiome 42 .We did not observe any significant microbiome diversity changes based on the immune status and length of ART.Nevertheless, the gut bacterial communities showed an enrichment of Succinivibrio in the PLWH with high CD4/CD8 ratio and long-term ART.Several earlier studies have reported that higher abundance of Succinivibrio in not only PLWH under ART 3,5,43 but also in untreated HIV positive elite controllers 43 .We also observed the enrichment of Ruminococcus gnavus in PLWH with low CD4/CD8 ratio, a bacterium associated with inflammatory bowel disease and known to produce imidazole propionate 44,45 .Imidazole propionate was recently linked to type 2 diabetes and cardiovascular risk in the general population 46 .The enrichment of Ruminococuus gnavus in individuals with low CD4/CD8 ratio may reflect the proinflammatory state associated with increased comorbidity risk present in these individuals 47,48 .Furthermore, in the oral environment we found an abundance of Megasphaera in PLWH with high CD4 + T-cell count and high CD4/CD8 ratio, as previously reported 49 .
Streptococcus was also significantly enriched in PLWH who were on short term ART and with low CD4/CD8 ratio.Likewise, several recent studies which explored the salivary microbiome, showed that the abundance of Streptococcus was increased in PLWH and associated with systemic inflammation 15,50,51 .
Intriguingly, we found an enrichment of Bifidobacterium and Faecalibacterium within the gut microbiome of individuals treated with INSTIs, a fact noteworthy even considering previous studies that reported an increase of Faecalibacterium in ART treated individuals 11,52 .It is plausible to speculate that the presence of these taxa in INSTI-treated individuals, in contrast to those treated with NNRTIs, could reflect their superior immune status or immune reconstitution, as previously proposed 53 .However, this association was not present in our study, suggesting the need for future prospective studies to further investigate this hypothesis.In the oral samples the genus of Veillonella was increased in INSTI-treated individuals and consequently in PLWH on DTG.Veillonella is an anaerobic bacterium, commonly found in the microbiota of the mouth, gut, and vagina.It has the ability to ferment lactic acid and use it as a primary source of energy.Alterations of Veillonella species in the gut microbiome have been reported in PLWH but not in connection to INSTI treatment 54 .In contrast, in the NNRTItreated group, we observed the presence of Gordoniobacter, Megasphaera and Fusobacterium in the gut and oral environment, respectively.Some Megasphaera species have the ability to ferment sugars and organic acids, including lactate, into volatile fatty acids such as butyrate, propionate, and acetate 55 .These short-chain fatty acids are essential for maintaining gut health as they serve as an energy source for colon cells and have antiinflammatory properties 56 .
Previous studies have demonstrated a link between ART regimens, specifically INSTIs, and obesity 34,35,57,58 .In our cohort, we identified an increased abundance of Bifidobacterium and Dorea in individuals with high BMI.These findings are particularly interesting since earlier studies have suggested an inverse association between Bifidobacterium and obesity, indicating a potential protective role of Bifidobacterium in weight gain, fat distribution and impaired glycemic control 59 .However, certain clinical studies have found an enrichment of Bifidobacterium in PLWH with high BMI, indicating the complexity of interactions within the microbiota of PLWH 60 .Conversely, studies have shown a higher prevalence of Dorea in HIV infected individuals with metabolic syndrome 61 .The presence of Dorea has been associated with insulin secretion and fasting blood glucose levels, implying its potential involvement in the progression of type 2 diabetes in overweight and obese individuals 62 .Intriguingly, our study also found an enrichment of proinflammatory pathobionts, such as Klebsiella and Escherichia-Shigella, in individuals with lower BMI.We observed a negative correlation between BMI and the presence of Klebsiella, Escherichia-Shigella, and Cloacibacillus, whereas a positive correlation was noted between BMI and the abundance of Bifidobacterium and Prevotella particularly in PLWH.In the oral microbiome of PLWH with overweight, there was a noticeable enrichment of Prevotella and Veillonella.Conversely, those subjects with a lower BMI exhibited an increased presence of Neisseria.Research within the field of dental medicine has earlier suggested that the oral microbiome of individuals with obesity is characterized by an escalation of traditional periodontal pathogens.However, the precise mechanisms driving these alterations remain to be elucidated 63 .
We acknowledge some limitations of our study.Since we have employed the 16S rRNA gene sequencing method, due to the homology between the sequences, 16S rRNA sequencing technique may not be able to distinguish related bacterial species 64 .Furthermore, we could not evaluate functional bacterial pathways thereby preventing a deeper understanding of the microbiome's metabolic activities and interactions and further discriminating cause-and-effect relationship.Another limitation of our study is the use of BMI as a marker for weight gain.While BMI is a surrogate marker for weight gain 65 , additional measurements such as waist circumference can provide a more precise assessment of obesity.Lastly, we only collected basic dietary information from participants; other factors, such as the types of nutrients consumed and lifestyle habits, were not recorded.Despite these limitations, our study involved the incorporation of a good number of participants, irrespective of our patient exclusion criteria.In addition, we ensured that the HC were carefully matched to PLWH in age groups and gender, strengthening the validity of our comparative analysis.
Overall, our study shows that there are associations between several components of fecal and oral microbiome in relation to different ART regimens and BMI in PLWH.We evidently demonstrate that the bacterial diversity was higher in HC compared to PLWH in both the gut and oral environment.We also observed several microbial markers associated with different ART treatments.Notably, the most prominent feature was the abundance of Bifidobacterium and Faecalibacterium in INSTI-treated individuals in the gut environment and Veillonella in the oral environment.The varying correlation of certain bacterial genera with BMI in both HC and PLWH might reflect how different health conditions, immune status, and host metabolism can influence the composition of the gut microbiota.Further research in this field will be valuable for better understanding of these cause-and-effect relationships and may provide insights for potential therapeutic interventions to optimize the gut microbiota in the context of obesity and HIV infection.

NFigure 1 .Figure 2 .
Figure 1.Alpha diversity and compositional changes in the gut and oral microbiome between Healthy Controls (HC, n = 80) and People living with HIV (PLWH, n = 69).(A) Boxplots showing the differences in the alpha diversity indices within the HC and PLWH in the gut and oral environment (B) NMDS plot illustrating the changes of beta diversity within the HC and PLWH in the gut and oral environment.Linear discriminant analysis effect size (LEfSe) analysis at the genus level showing the differentially abundant microbiota between HC and PLWH in the (C) gut and (D) oral samples, respectively.

Figure 3 .
Figure 3. Differences in the gut and oral microbiome in PLWH, further divided into two groups based on BMI.Linear discriminant analysis effect size (LEfSe) analysis showing the significant microbial organisms between individuals with high and low BMI (< / ≥ 25 kg/m 2 ) in the gut and oral environment: differences (A) in PLWH (high BMI n = 37, low BMI, n = 32), and (B) in PLWH treated with DTG (high BMI n = 24, low BMI n = 17).Spearman correlations showing the association between BMI and microbial composition at the genus level within the gut and oral environment in the (C) whole cohort and (D) PLWH.

Table 1 .
Baseline demographic and clinical characteristics of the study participants.Table1the Mann-Whitney U test was applied to compare the continuous variables and Fisher's exact test to analyze the categorical variables.All baseline characteristics are illustrated as median (inter quartile range) and demographic characters are illustrated as n (%).*Denotes that these individuals were not included in the calculation of p-values.# Denotes that out of 69 individuals in PLWH, 7 individuals had HIV RNA level > 50 (c/ mL) with range 53-132.